Dimension reduction of high-dimensional microbiome data facilitates subsequent analysis such as regression and clustering. Most existing reduction methods cannot fully accommodate the special features of the data such as count-valued and excessive zero reads. We propose a zero-inflated Poisson factor analysis (ZIPFA) model in this article. The model assumes that microbiome read counts follow zero-inflated Poisson distributions with library size as offset and Poisson rates negatively related to the inflated zero occurrences. The latent parameters of the model form a low-rank matrix consisting of interpretable loadings and low-dimensional scores which can be used for further analyses. We develop an efficient and robust expectation-maximization (EM) algorithm for parameter estimation. We demonstrate the efficacy of the proposed method using comprehensive simulation studies. The application to the Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS) provides valuable insights into the relation between subgingival microbiome and periodontal disease. This article is protected by copyright. All rights reserved.

中文翻译：

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应用于微生物组读取计数的零膨胀泊松因子模型

高维微生物组数据的降维有助于后续分析，例如回归和聚类。大多数现有的归约方法不能完全适应数据的特殊特征，例如计数值和过多的零读取。我们在本文中提出了一个零膨胀泊松因子分析 (ZIPFA) 模型。该模型假设微生物组读取计数遵循零膨胀泊松分布，其中库大小作为偏移量，泊松率与膨胀零出现负相关。模型的潜在参数形成一个低秩矩阵，由可解释的载荷和低维分数组成，可用于进一步分析。我们开发了一种用于参数估计的高效且稳健的期望最大化 (EM) 算法。我们使用综合模拟研究证明了所提出方法的有效性。口腔感染、葡萄糖不耐受和胰岛素抵抗研究 (ORIGINS) 的应用为了解龈下微生物组与牙周病之间的关系提供了宝贵的见解。本文受版权保护。版权所有。